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A new analysis from Epoch AI, a nonprofit AI research institution, suggests the artificial intelligence sector may soon face constraints in achieving significant performance improvements with reasoning AI models. The report indicates that advancements in reasoning models could decelerate possibly within the next year.
Slowing Progress in Reasoning AI Models Predicted
Reasoning models, such as OpenAI’s o3, have demonstrated considerable enhancements in AI benchmarks recently, particularly in assessing mathematical and programming aptitudes. These models utilize increased computational power to tackle problems, improving efficacy, albeit at the expense of longer processing times compared to traditional models.
The Development of Reasoning Models
The development of reasoning models involves initially training a conventional model on extensive datasets. This is succeeded by applying a reinforcement learning technique, which provides the model with “feedback” on its solutions to complex problems.
According to Epoch, leading AI laboratories like OpenAI have not yet committed substantial computational resources to the reinforcement learning phase of reasoning model training.
However, this is evolving. OpenAI has reported allocating approximately 10 times more computing power to train o3 compared to its predecessor, o1, with Epoch speculating that the majority of this increase was directed toward reinforcement learning. OpenAI researcher Dan Roberts recently disclosed the company’s future plans emphasize using even greater computing capacity for reinforcement learning, surpassing that used for initial model training.
Epoch suggests that there are limits to how much computing can be effectively applied to reinforcement learning.
Potential Convergence by 2026
Josh You, an analyst at Epoch and the author of the analysis, elucidated that performance gains via standard AI model training are currently quadrupling annually, whereas performance gains from reinforcement learning are escalating tenfold every three to five months. He projects that the advancement of reasoning model training will “probably converge with the overall frontier by 2026.”
Epoch’s analysis incorporates several underlying assumptions and draws partly on public statements made by AI company executives. Crucially, the report posits that scaling reasoning models may present challenges beyond mere computational power, including substantial overhead expenses associated with research.
Overhead Costs and Scaling Challenges
You articulates, “If research incurs persistent overhead costs, the scaling of reasoning models might not reach anticipated levels. The rapid scaling of compute is potentially a crucial element of advancement in reasoning models, making it essential to monitor closely.”
Evidence indicating that reasoning models may approach limitations in the near term will likely concern the AI community, which has heavily invested in their development. Studies have previously highlighted drawbacks of reasoning models, including high operational costs and a propensity for increased hallucination relative to certain conventional models.